Many of the outcomes in seminal cancer studies are highly skewed. Moreover, the data are clustered within oncologist, practice, or hospital, and often the outcomes are right or interval censored, and there are a large number of predictors of interest. Because of the skewness in the outcomes, medians and quantiles of the outcome as a function of covariates is of interest. There is very limited current literature available to deal particularly with statistical models and analysis of clustered skewed response data. Here, to analyze such data, we propose methods in five aims that will have a high impact on clinical and biostatistical sciences and future cancer studies. In particular, the four aims are: 1).Quantile regression for highly skewed clustered outcomes (censored and not censored); 2) Methods for interval-censored data with a log-linear median; 3) Estimating covariate effects on quantiles of highly-skewed mixed response data (including zero-inflated type models); 4) Estimation and prediction for skewed responses when there are a large number of covariates. An additional goal is to make the newly developed statistical/epidemiological methodology widely accessible to nonstatisticians. For the methods described in each aim, we plan to create macros and procedures which can be used with existing, widely-used statistical packages (e.g., SAS and R). Statistical macros and procedures will be made available on our website, together with documentation on how to apply these macros to the examples analyzed in the resulting publications. The approaches we propose are specifically developed to answer important clinical questions that our clinical collaborators need to publish future clinical papers.